The University of Massachusetts Amherst
University of Massachusetts Amherst

Search Google Appliance

Links

ECE Researchers Publish Article About Six Promising Technologies That “Can Potentially Reshape the Computing Paradigm”

ECE Professors Qiangfei Xia and J. Joshua Yang

Qiangfei Xia and J. Joshua Yang

A paper in Advanced Materials Technologies, published by a team from the Electrical and Computer Engineering (ECE) Department, reviews the current state of six of the most promising technologies for creating new types of memory devices that can replicate the function of biological neurons and synapses. The paper was also reviewed in The Next Platform.

“This paper discusses various emerging nanoscale electronic devices that can potentially reshape the computing paradigm in the near future,” the ECE authors declare in their Advanced Materials Technologies piece.

The six types of devices reviewed include resistive random‐access memory (ReRAM), diffusive memristors, phase change memory (PCM), spintronics-based magneto-resistive random‐access memory (MRAM), ferroelectric field-effect transistors (FeFETs), and synaptic transistors

As the review in The Next Platform explains, “Overall, these technologies have the potential to ‘significantly accelerate computing speed while reducing power consumption,’ write the authors, who also admit that each has its own specific strengths and weaknesses. They believe any artificial neuromorphic systems will still have to rely on CMOS circuitry for peripheral components, at least for the foreseeable future.”

The paper in Advanced Materials Technologies, titled “Emerging Memory Devices for Neuromorphic Computing,” was written by ECE doctoral student Navnidhi K. Upadhyay, UMass student Hao Jiang, ECE post-doctoral research fellow Zhongrui Wang, and ECE doctoral student Shiva Asapu, along with ECE Professors Qiangfei Xia and J. Joshua Yang.

As the six authors write in their abstract, “A neuromorphic computing system may be able to learn and perform a task on its own by interacting with its surroundings. Combining such a chip with complementary metal–oxide–semiconductor (CMOS)-based processors can potentially solve a variety of problems being faced by today’s artificial intelligence systems.”

Fortunately, as the authors observe, many emerging memory devices can naturally perform vector matrix multiplication directly utilizing Ohm’s law and Kirchhoff’s law when an array of such devices is employed in a cross-bar architecture. With certain dynamics, these devices can also be used either as synapses or neurons in a neuromorphic computing system.

“To make the neuromorphic system self‐reliant these new device technologies must improve by leaps and bounds,” the authors conclude. “There still exist areas for continued development, thus moving forward with a strong collaboration among material scientists, device engineers, hardware designers, computer architects, and programmers will help to facilitate the cross‐disciplinary dialogue to solve many challenges being faced by neuromorphic community.” (February 2019)